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Throughout the processing and analysis of survey data, a ubiquitous issue nowadays is that we are spoilt for choice when we need to select a methodology for some of its steps. The alternative methods usually fail and excel in different data regions, and have various advantages and drawbacks, so a combination that unites the strengths of all while suppressing the weaknesses is desirable. We propose to use a two-level hierarchy of learners. Its first level consists of training and applying the possible base methods on the first part of a known set. At the second level, we feed the output probability distributions from all base methods to a second learner trained on the remaining known objects. Using classification of variable stars and photometric redshift estimation as examples, we show that the hierarchical combination is capable of achieving general improvement over averaging-type combination methods, correcting systematics present in all base methods, is easy to train and apply, and thus, it is a promising tool in the astronomical “Big Data” era.
We demonstrate the eclipsing binary detection performance of the Gaia variability analysis and processing pipeline using Hipparcos data. The automated pipeline classifies 1 067 (0.9%) of the 118 204 Hipparcos sources as eclipsing binary candidates. The detection rate amounts to 89% (732 sources) in a subset of 819 visually confirmed eclipsing binaries, with the period correctly identified for 80% of them, and double or half periods obtained in 6% of the cases.
We started a systematic search for periodic variable-star candidates in the EROS-2 database in the context of preparatory work for the Gaia satellite mission. The goal is to evaluate different classification tools and strategies, and to identify a large sample of variable candidates. In this paper we present the results of an assessment study of a three-step identification and classification process. In the study we took a sample of about 80,000 stars from one of the LMC EROS fields.
Two upcoming large scale surveys, the ESA Gaia and LSST projects, will bring a new era in astronomy. The number of binary systems that will be observed and detected by these projects is enormous, estimations range from millions for Gaia to several tens of millions for LSST. We review some tools that should be developed and also what can be gained from these missions on the subject of binaries and exoplanets from the astrometry, photometry, radial velocity and their alert systems.
The ESA Gaia mission will provide a multi-epoch database for a billion of objects,
including variable objects that comprise stars, active galactic nuclei and asteroids. We
highlight a few of Gaia’s properties that will benefit the study of variable objects, and
illustrate with two examples the work being done in the preparation of the data processing
and object characterization. The first example relates to the analysis of the nearly
simultaneous multi-band data of Gaia with the Principal Component Analysis techniques, and
the second example concerns the classification of Gaia time series into variability types.
The results of the ground-based processing of Gaia’s variable objects data will be made
available to the scientific community through the intermediate and final ESA releases
throughout the mission.
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